Electro-optical method and apparatus for evaluating protrusions of fibers from a fabric surface

ABSTRACT

An electro-optical method and apparatus for evaluating the dimensions of any protrusion from the threshold of the fabric surface is achieved by bending any length of fabric over a rotating roller so that the contoured area of the protrusion body above the surface can be visualized. The image of the silhouette as seen by a digital camera is processed by image processing algorithms then processed statistically and then by a neural network to yield an integrated picture of the fabric protrusions. The grading results of pilling are well correlated to the human visual method of pilling evaluation.

RELATED APPLICATIONS

This application is a Continuation-in-Part (CIP) of U.S. applicationSer. No. 10/601,858 filed on Jun. 24, 2003, which in turn claims thebenefit of provisional application Ser. No. 60/390,465, filed on Jun.24, 2002, which is incorporated in its entirety by reference herein.

BACKGROUND OF THE INVENTION

Fabric pilling is a serious problem for the textile and the apparelindustry. Pilling is a fabric surface fault in which “pills” ofentangled fibers protrude from the fabric surface. They give a badappearance and can sometimes deteriorate the properties of the fabric.The development of surface hairiness may be an important factor indegrading the quality of certain fabrics and papers.

Due to the importance of the subject, the process of pill formation infabrics by rubbing action has been thoroughly investigated.Consequently, there are many different test methods that have beendeveloped to determine the resistance of fabrics to pilling. Themeasurement of pills is performed in two stages. The first entails theformation of pills by means of a laboratory test apparatus—allpill-formation apparatus is based on either tumbling or abrading thetest specimen. The second stage is the evaluation of the pilling degreeby subjective methods. This is done by comparing the pilled samples witha set of standard photographs or holograms that portray samples offabrics with various degrees of pilling severity. Other methods involvethe manual counting and weighing of the pills.

The pilling standards that are used to grade the samples of testedfabric are on the following scale: 5=no pills; 4=slight pilling;3=moderate pilling; 2=severe pilling; 1=very severe pilling

The development of an objective method of pill, fuzziness, snag andoverall general grading is a valuable contribution to the field offabric testing.

Methods and apparatus for inspecting fabric surfaces are quite common.Lane in U.S. Pat. No. 5,774,177 describes an apparatus for automaticallydetecting defects within the field of view of a video camera. The imagereceived is then processed by a blob analysis to identify the defects.Vachtsevanos et al, in U.S. Pat. No. 5,936,665 describes an automatedapparatus for counting “pills” in textile fabrics. This patent utilizesa CCD camera to capture an area of the fabric surface. The image of thesurface is then processed for counting the “pills” and the data isfuzzified to determine the membership of the data in one or more of aplurality of fabric classes. In these examples and other, an area of thetested fabric is illuminated, captured by electro-optical apparatus andthe data is processed to yield the characteristic data of the fabricsurface.

SUMMARY OF THE INVENTION

One purpose of the present invention is to establish system, apparatusand method for automatic and objective grading of fabrics so that theresulting grading will imitate grading done by a human being. Fabricgrading performed by a human examiner results several specific gradingscores (for features such as pilling, fuzziness, general (pilling andfuzziness jointly) and snagging grading scores. The scores are in thespan of 1-5 where 1 indicates a low score, that is the lowest (orinferior) quality and 5 a high score, which is the highest quality. Thepresent invention relates to imperfections protruding from the fabricsurface such as fibers, fuzzy, pills and yarns. The method involved isbending the fabric so that the examined surface would be examined from aside view, viewing the silhouette of the fabric at the bent line, atdefined increments as it progresses along the fabric. The analysis ofthe image becomes one dimensional, that is the silhouette being takendepends on the width coordinate only and does not depends on thelongitudinal coordinate of the specimen, and the problem of detectingall the objects on the surface of the fabric is resolved by imageprocessing methods performed on a series of consecutive silhouetteimages. The grading of the pilling, fuzziness, hairiness, snags andoverall general severity may be carried out using a Neural Networkand/or Fuzzy Logic based classification.

The present invention describes a method, a system and an apparatus forgrading the severity of the above-listed effects, such as pilling orhairiness of fabric samples that were exposed to surface friction, suchas rubbing. The fabric sample is folded along its width by means of arotating tube. A digital sensor, such as a CCD array, captures theprofile of the fold and transmits the profile images to a computingunit. The fabric is further moved around the rotating tube in smalldefined increment and another picture is taken. This process continuesuntil the complete sample area is scanned. The data is processed byimage processing methods and the results are transformed into theexisting grading scale so that the computed grading is expressed usingthe scale traditionally used by an expert examiner.

BRIEF DESCRIPTION OF THE DRAWING

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 illustrates the basic components of the pilling grading system;

FIG. 2 illustrates the elements and the structure of the Profile CaptureUnit;

FIG. 3 is a presentation of a schematic simplified flow chart of thepilling evaluation procedure;

FIG. 4 is a presentation of a schematic simplified flow chart of theImage Processing Unit;

FIGS. 5A-5C illustrate a grey scale image of the typical profile, acorresponding black and white image and corresponding image on which theBL, Th lines and vertical separation lines between protrusions aremarked, according to some embodiments of the present invention;

FIG. 5D illustrates the image of the typical profile in thecross-section of fabric on the output of Profile Capture Unit, accordingto some embodiments of the present invention;

FIG. 6 illustrates a typical graph showing the changes in standarddeviation of image brightness along a line substantially vertical to thefabric latitudinal line, according to some embodiments of the presentinvention;

FIG. 7 illustrates the histograms of the fabric brightness and grades inthe Data Set, according to some embodiments of the present invention;

FIG. 8 illustrates the structure of the Neural Network for pillinggrading, according to some embodiments of the present invention;

FIG. 9 illustrates the histogram for HP grades for an original fabricobtained by expert and neural network, according to some embodiments ofthe present invention;

FIG. 10 illustrates the histogram for HP grades for tested fabricobtained by expert and neural network for M&S P18A Test Method,according to embodiments of the present invention;

FIG. 11 illustrates the histogram for HP grades for tested fabricobtained by expert and neural network for M&S P18B Test Method,according to embodiments of the present invention; and

FIG. 12 illustrates the typical fabric images showing upper and lowerlimits of hairiness, according to embodiments of the present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

This is an invention of a method and apparatus for inspecting thesurface of fabrics that have been exposed to friction and as a resultmay have been damaged. The damage caused is depicted by {fibers, pills,fuzziness, and snags protruding from the said surface. The assessment ofthe severity of the damage of fabric surface defects must take intoaccount the size and the number of protrusions and number of otherparameters of silhouettes of the fabric. The present grading system isbased on a subjective comparison of the damaged fabric surface to a setof standard photographs or holograms that rank the damage from verysevere (scale 1) to no damage (scale 5). It is beneficial to adjust anynew method of damage evaluation or grading method to the traditionalscale.

Attention is made to FIG. 1, which is schematic block diagramillustration of the structure of a grading device 50 according toembodiments of the present invention. Grading device 50 may compriseProfile Capture Unit 52 which may photograph at least several profilesof the bend in the fabric in defined increments and may transmit thedata representing these profiles to mage Processing Unit 54. The outputdata of Image Processing Unit 54 may be fed into Pilling Grading Unit 56that may integrate the results of the fabric surface protrusion, asreceived from Image Processing Unit 54, into a grading system andmethod, using Neural-Network (not shown in this drawing) and aparameters database to correlate the grading results of system 50 to theexisting manual grading scale.

Attention is made now to FIG. 2, which is a schematic illustration of aProfile Capture Unit 20, according to some embodiments of the presentinvention. Profile Capture Unit 20 may comprise a conveyer belt 2, whichmay be powered by motor 4 and may turn around its two end pulleys 3 aand 3 b each of about 1 cm. in diameter, a digital optical sensor 5 suchas digital camera, a screen 6 placed so as to form a background of animage taken by said digital camera 6 and a computing and control unit 7,operably connectable to motor 4, background screen 6 and digital camera5.

A strip of fabric 1 that was pre-treated to induce pilling by a standardmethod (such as ASTM 3512-99 or B.S. 581186 or M&S P18A or other) may beattached to conveyer belt 2 by any suitable means, such as clips (notshown in the drawing). The width of the conveyer belt may typically be15 cms and its linear length between its two pulleys may typically be 40cms, so as to accommodate for a standard examining strip of fabric. Theconveyer belt surfaces may be placed at an angle with respect to theline of sight 5A of digital camera 5. At one of pulleys 3 a or 3 b maybe powered motor 4, which may be a step motor that may rotate the pulleyto drive belt 2 in linear increments the length of which may becontrolled by computing unit 7 in the range of, for example, 0.1 to 0.5cms.

Digital camera 5 may be placed so that its optical system is within thefocal distance of the top of the fabric that has been wound onto the toppulley 3 b. Screen 6 may be placed behind the top of the fabric surfacewith respect to digital the LOS of camera 5 and may serve as abackground to the silhouette of the fabric line above the upper pulleyas captured by digital camera 5. The color of the front surface of thescreen that is facing the camera lens can be changed responsive tocontrol signal received from computing unit 7 to contrast the color oftested fabric 1. If necessary, screen 6 can be translucent and theillumination of the sample would be projected on the back surface ofbackground screen 6. Computing unit 7 may synchronize the movement ofconveyer belt 2 to the exposures of digital camera 2. After eachexposure and capturing of an image, the conveyer belt may be moved toforward tested fabric 1 by a preset increment. Typically a capture of animage of the silhouette of the bent fabric is taken every 2.5-5 mm alongthe longitudinal dimension of the tested fabric yet, other desiredincrements may be used. The resolution of the captured image istypically in the order of 17-20 pixels per mm (ppm).

Computing unit 7 may run an executable program able to determine andmark three different areas in the captured image: area of fabric (AF),area of protrusions (AP) and area of background (AB). According to someembodiments of the present invention these areas, as well as othercharacteristic features of a tested fabric, may be extracted from and/orcalculated based on the images taken by Profile Capture Unit 20. Area ofthe fabric (AF) is considered as the bottom part of the image where thefabric is imaged. Area of the protrusions (AP) is considered the areabetween AF and the background, where the fabric has a substantive amountof pills, hairiness, fuzziness and like. The area of the background (AB)is the area in the image above the AP and on the sides of the AF and AP.

Attention is made now also to FIG. 3 which presents a flow chart of thepilling evaluation procedure, and to FIGS. 5A-5D which are illustrationsof a grey scale image of the typical profile, a corresponding black andwhite image, a corresponding image on which the BL and Th lines andvertical separation lines between individual protrusions are marked andillustration of the image of the typical profile in the cross-section offabric on the output of Profile Capture Unit 52, respectively, accordingto embodiments of the present invention. Profile capturing stage (block302) and framing stage (block 304) have been described above, withrespect to the detailed description of profile capture unit 20. A nextstage may be Thresholding (block 306). The captured image of the testedfabric's silhouette is first transferred to Gray Scale Image where theprofile is captured. The outliers are then determined. It is thentransferred to Black and White Image. The area of the outliers definedin the previous step is processed by mathematical and statisticalanalysis to obtain a two-dimensional evaluation, as explained in moredetail below. The next exposure may be analyzed in the same way but theprogram now may take into account the previous exposure(s) and may thusidentify the protrusions that appeared in it. Sequential exposures ofthe fabric fold may reveal a gradual increase and then decrease in thecross-section of a protrusion located at a certain location along thesilhouette image. Hence, the actual size of the protrusions, in twodirections, can be determined. The data obtained from the series ofexposures of the advancing folds in the fabric enables an accuratemeasurement of the number of protrusions, the size of the individualprotrusions and their density (number per unit area of fabric). This isthe essential data required for assessing damage to the fabric fromprotrusions such as pilling.

At this stage the operator may determine the parameters of the ImageProcessing program such as the number of profiles, the parameter of theaverage smoothness, and the threshold for the outliers. The length,height, area and the distance between the outliers for each specificprofile increase as a result of the image processing. This stage is theadjusting stage (block 308).

In the image processing stage (block 310) parameters such as the numberof protrusions beyond the base line (BL) and the average area ofprotrusion may be calculated, as will be described in details below. Asshown in FIG. 5C BL and Th lines may be calculated and marked on animage of a silhouette of the tested fabric. FIG. 5D depicts theseparation of the silhouette image area into AP, AF and AB areas.

The next stage is Statistical Processing (block 312). The purpose ofthis stage is to calculate the average values and the standarddeviations for the outliers' parameters for the entire set of profilesof the fabric sample. The results of the Statistical Processing aretransmitted to a Neural Network (block 314). The Neural Network hasspecific training data set, and can determine the pilling gradingaccording to the existing scale, as will be detailed herein below.

Attention is made now to FIG. 4, which describes the Image Processingstage (block 310 in FIG. 3) in some detail. This stage provides anumeric analysis of each identified outlier for each specific profile.In addition, the distance between every adjacent outlier along a profile(in the cross direction of fabric) is calculated for each specificprofile. Sequential analysis of the ordered assembly of the profilesenables the program to determine the length and the height of theoutliers in the longitudinal direction.

A tested fabric may substantially be uniquely characterized by a set ofvalues given to a corresponding set of characteristic parameters.Characteristic parameters of the tested fabric may comprise:

Base Line (BL). Base Line (BL) may be defined the border line between AFand AP. Detection of the BL may be done based on analysis of thestandard deviation of the brightness within a horizontal rectangle areadescending from the upper end of the image and extending to the bottomline of the image, and including the zone in which the fabric isrepresented. When calculating the value of the standard deviation (σ) ofthe brightness in the upper part of the rectangle, where AB is the valueof σ is typically low. When the location inside the rectangle areaapproaches to the area of protrusions AP the value of σ of thebrightness significantly increases and as the location in the rectanglefurther approaches the bottom border line the value of σ decreasesagain, as depicted in FIG. 6 to which reference is now made. FIG. 6illustrates a typical graph showing the changes in standard deviation ofimage brightness along a line substantially vertical to the fabriclatitudinal line, according to some embodiments of the presentinvention. Thus, the border line BL may be indicated as the location foreach such narrow rectangle where the value of σ reached a specificpercentage, marked RP in FIG. 6, of local maximum, also indicated as aRaising Point (RP). The value of ca and the value of the imagebrightness at the RP point are used for the determination of the localBL location. Further to the determination of the BL, the executableprogram may provide for, based on the value of a and the value of theimage brightness, the calculation of:

-   -   i. Alignment of the image brightness along the silhouette        profile,    -   ii. Filtration of the background for the purpose of elimination        of point-noise from the background area (also called “background        whitening”),    -   iii. Moving average for the purpose of eliminating “base        fluctuation” and detecting the actual border between the        Protrusion Area and the Fabric Area.

Threshold (Th): The detection, calculation and indication of thelocation of BL and background whitening allow the separation of the AParea from the FA area. The AP area typically contains protrusions of theoriginal fabric which are marked PF, pilling which are marked P andfuzzing which are marked F. The image of the AP area typically containssections with P protrusions, with F protrusions and sections withseparate fibers or small groups of fibers which may not be classified asP or F protrusions. The latter type of protrusions is to be eliminatedfrom the calculations relating to characteristic parameters. Thisseparation may be carried out based on a geometrical shape anddimensions (width and height of the image of a single protrusion) forfiltering of the protrusions. For this purpose a first, geometricalThreshold (Th) may be determined so that if the geometrical features ofan examined protrusion is located under the Th value that protrusionwill be marked non-relevant to the analysis. The value of Th isdetermined during the adjustment of the image processing algorithms andthe training of the Neural Networks. In order to separate P protrusionsfrom F protrusions a value of a second threshold (Th_(P-F)) may becalculated. The value of the characteristic for a certain Th_(P-F) of atested fabric may be calculated, among other things, based on the factthat for a P protrusion the dimensions are, typically, higher andnarrower than those of a F protrusion. Another possible method forsetting a value for Th_(P-F) may be based on the fact that typically thebrightness (typically measured in a 1-256 grey levels) of P-protrusionis lower than that of F-protrusion. Thus, for example, for a specificfabric it may be found that protrusions with brightness level up to 20will be marked as P and protrusions with brightness level from 21 to 35will be marked fuzziness and protrusions with brightness level higherthan 40 will not be considered at all as protrusions.

In this approach for solving separation between P-protrusion andF-protrusion the grey scale a GI index for the threshold Th_(P-F) may becalculated according to the equation:${GI} = {{\frac{X - F}{T_{G} - F}\quad 0} \leq {GI} \leq 1}$Where:

X—protrusion brightness

F—fabric brightness

T_(G)—Grey scale threshold that is used for Baseline determination

If GI≧T_(GI) the protrusion is classified as Fuzzy. Otherwise it will beclassified as a Pill. T_(GI) is the Grey Scale Threshold for theseparation between P protrusion and F protrusion.

The border between the fabric and the background, and also the thresholdline of the fabric fold—that is the line that would be seen when allsurface obstacles are removed. The border between the fabric and thebackground may relate to the “top” line of the image of a silhouette andto the side lines of same. Parameters that can be extracted from the topline of the image of the silhouette are an average height of the topline and its standard deviation. Use of these parameters may assist indetermining border line in accuracy of up to −/+1 pixel. Once the borderline for a silhouette is determined computing unit 7 may further performcropping process to ‘remove’ portions of the image extending out of theborder line.

The decision of the position of the threshold line is also based on theaverage height of the silhouette. The software processes any protrusionabove the threshold line.

Additional characteristic parameters: In order to facilitate automatedgrading of a fabric the apparatus and method according to the presentinvention may use additional characteristic parameters, which may beextracted from the silhouette image or calculated based on extracted orcalculated parameters. Such additional parameters may comprise:

C—The number of protrusions beyond the baseline

W—Protrusion width

H—Protrusion height

A—Protrusion area

S—Standard deviation of the distance between protrusions

B—Fabric brightness

Parameters C, W, H, A and S may be determined as an ensemble average.The vector of protrusion parameters {right arrow over (P)}_(P) is givenby the formula:{right arrow over (P)}_(P)=[C,W,H,A,S,B]^(T)

Hairiness, hairiness area: According to an embodiment of the presentinvention the apparatus and method of the present invention may be usedto estimate the height and the average brightness of the hairiness areaAH. The average value of the Base Line BL, that is the average value ofthe height of the base line above a reference horizontal line across thewidth of the sample fabric, may be used as the lower border (i_(B)) ofthe AH area. The upper border (i_(U)) of the AH area may be determinedby calculating the standard deviation of brightness of the rows close tothe border between AB and AP:${{\sigma(i)} = ( {\frac{1}{N}{\sum\limits_{j = 1}^{N}( {{B( {i,j} )} - {\overset{\_}{B}( {i,j} )}} )^{2}}} )^{1/2}},,{i = \overset{\_}{1,M}}$Once the value of σ(i) is calculated, it may be compared to threshold(1+T_(H))σ_(SL):Δ(i)=σ(i)−(1+T _(H))σ_(SL) ,i= 1,MThe i_(U) row where Δ(i) becomes nonnegative may be used to determinethe upper border of the AH area. The height, as measured vertically withrespect to a latitudinal line may then be given by:H _(H)=−(i _(I) −i _(B))The average brightness of the AH area may be calculated by formula:${\overset{\_}{B}}_{H} = {\frac{1}{N}{\sum\limits_{j = 1}^{N}{\frac{1}{M - {{BL}(j)}}{\sum\limits_{i = i_{H}}^{M - {{BL}{(j)}}}{B( {i,j} )}}}}}$The hairiness index (I_(H)) may be determined as a linear convolution ofthe height and average brightness of the AH area:${I_{H} = {{\alpha \cdot \frac{{\overset{\_}{B}}_{H}}{F}} + {( {1 - \alpha} ) \cdot \frac{H_{H}}{L}}}},\quad{0 \leq I_{H} \leq 1},$where:L—normalization factorF —fabric brightnessThe conversion of the Hairiness grading into the expert grading scalecan be produced by the division of the definition range of the I_(H)index into five subintervals:G_(H)=1, if I_(H1)≦I_(H),G_(H)=2, if I_(H2)≦I_(H<I) _(H1),G_(H)=3, if I_(H3)≦I_(H)<I_(H2),G_(H)=4, if I_(H4)≦I_(H)<I_(H3),G_(H)=5, if I_(H)<I_(H4).The index of hairiness value of the I_(H), that determines the limits ofthe subintervals, must be selected so that the probability ofdifferences between the expert grade of hairiness and the gradedetermined according to the present invention do not exceed apermissible value.

Snag Detection: Snag (S) detection has some specific properties and itdiffers from Pills (P) and Fuzzy (F) detection. The main difference isthat the dimensions (scale) of Snags as a rule are much bigger than thedimensions of Pills and Fuzzy. As a result of this, errors of thebaseline determination and geometrical parameters of protrusions have aweak influence on the accuracy of S-grading. Another difference is thatthe Snag is a singular effect and is not mixed with Pills, Fuzzy andHairiness effects. Thus there is no need to separate different types ofprotrusions when detecting Snags. Furthermore, according to Marks &Spencer P21A Test Method Snagging grading relates to the testedspecimens only since there are no snags in the original fabric. As aresult of this, the algorithm for snagging evaluation may represent asimplified modification of the HP, P, and F algorithm. The S-algorithmis based on two parameters that are obtained at the Image Processingphase of a set of profile images:

1. Number of protrusions beyond the baseline—C

2. Average area of protrusion—A

Snagging Index (I_(s)) is determined as a linear convolution of thesetwo parameters:I _(S) =α·C+(1−α)·AThis Snagging Index is the basis for the snagging grading analogous to Hgrading on the basis oh Hairiness Index:G_(S)=1, if I_(s1)≦I_(S)G _(S)=2, if I_(S2)≦I_(S)<I_(S1)G_(S)=3, if I_(S3)≦I_(S)<I_(S2)G_(S)=4, if 0<I_(S)<I_(S3)G_(S)=5, if I_(S)=0The limits of the subintervals I_(Si), i=1,2,3 are determined as aresult of a comparison analysis of the expert grades and the parametersobtained by Image Processing. The algorithm for grading of snag lengthis built in the same manner. Statistical analysis of the results of forthe sample of 38 woven and single knitted fabrics, showed that thelength and area of snags have a very high correlation. However, takinginto account that the calculation of the snag area is simpler than thecalculation of the snag length, the snag area has been used for theestimation of the snag length. A three-level scale is used for the snaglength grading: S—short, M—medium, and L—long. The algorithm for gradingof snag length according to the present invention in an expert scale isas followsG_(L)=L, if A≧A_(L)G_(L)=M, if A_(S)≦A<A_(L)G_(L)=S if A<A_(S)

Grading of a tested fabric according to embodiments of the presentinvention may need a new approach towards the computing platform that isrequired. Grading is an estimation of grade—G (it can be P grade G_(P),F grade G_(F), or HP grade G_(HP)) in accordance to the results of ImageProcessing phase, according to the formula:G=A(P _(P)),Where: A is the grading operatorFor the HP, P, and F grading the operator A can be calculated by aNeural Network computing platform. The Neural Network may implement aconvolution of the parameters obtained in the Image Processing phase tothe required grade—G. To construct and train the Neural Network it maybe necessary to prepare a Data Set that may contain the parameters ofprotrusions (vector P_(P)) and the expert grades for a sufficientrepresentative sample of fabrics. When preparing the Data Set it isbeneficial to achieve a uniform representation of all grades and all thedifferent color shades (dark, medium, and light). It is evident that theData Set should be built up from a homogeneous group of fabrics. Forexample, it can be a group of single knitted fabric (Marks & Spencer,P18A Test Method—Random Pilling Drum) or a group of knitwear (Marks &Spencer, P18B Test Method—Random Pilling Drum—Reverse Action).

Table 1 below presents an example of a part of the Data Set with testspecification and expert grades for a specific fabric TABLE 1 FabricExpert Grades Code Revolutions Specimen HP P F kp-34 0 Original 4 800Tested 1 1 1 1 1 1 2 1 2 1 1 1

Table 2 below presents an example of a part of the Data Set with fabricbrightness—B and protrusion parameters: TABLE 2 Pilling FuzzinessGeneral B P_C P_W P_H P_A P_D F_C F_W F_H F_A F_D G_C G_W G_H G_A G_D 270 0 0 0 0 1.56 0.71 0.37 0.12 0.13 1.56 0.71 0.37 0.12 0.13 25 1.6 1.210.48 0.39 0.16 9.99 1.52 0.54 0.33 0.8 11.59 1.53 0.56 0.35 0.83 26 1.671.42 0.36 0.34 0.13 9.15 1.56 0.56 0.33 0.76 10.82 1.63 0.55 0.36 0.8 373.85 1.68 0.57 0.49 0.38 6.36 1.68 0.5 0.3 0.63 10.21 1.73 0.55 0.390.74 36 2.38 1.57 0.63 0.47 0.21 8.64 1.39 0.53 0.29 0.76 11.02 1.450.55 0.33 0.81Where:P_(—C), F_C, G_C are the average number of pills, fuzziness, and thesum, respectively. P_W, F_W, G_W are the average width of protrusions P,F and G, respectively, P_H, F_H, G_H are the average height ofprotrusions P, F and G, respectively, P_D, F-D, G_D are the standarddeviation of the distances between protrusions P, F and G, respectively.

The preparation of the Data Set according to the present inventionshould be carried out so as to ensure a uniform distribution of alllevels of grades. Prior to this the grades are unknown from acorresponding testing session made by a traditional human expert and thesampling for the Data Set takes place after the physical testing andexpert grading. FIGS. 7A-7D present sample histograms for HP, P, and Fgrades and for fabric brightness, respectively, obtained for the Marks &Spencer M&S P18A and P18B Test Methods. Amongst these sample histogramsthe only the histogram for fabric brightness—B may be considered asuniform. This is quite usual as the sample for fabric brightness isdetermined before physical testing phase. The neural network that hasbeen built and trained, for example, on the base of the F-gradehistogram for Marks & Spencer P18A Test Method, is oriented to gradeG_(F)=2 and demonstrates the best correlation to the expert grades forthe grades G_(F)=1 or G_(F)=3. On the other hand, these histograms canshow the actual proportion between the different levels of the HP, P,and F grades. Based on this, the histograms shown in FIGS. 7A-7D havebeen used for building and training the neural network for grading.

Attention is made now to FIG. 8, which illustrates a structure of aNeural Network computation platform 80 for pilling grading. The NeuralNetwork computation platform 80 may perform the computations involved inmatching manual grading to the set of calculated parameters of a testedfabric. The grading of the surface evaluation of textile fabrics on thebase of the vector Pp is a classification issue. One of the mosteffective approaches to carry out this kind of classification may bedone with Neural Network computation platform 80. An interpretation of aNeural Network computation platform 80 output is an estimate ofprobability of grade, in which case the network actually learns matchingof human-grade to a set of parameters which represent a tested fabric.The mission of building and training a Neural Network can be formulated,according to embodiments of the present invention, as the mission ofestimation of a probability density function of the human-grades. If anapproximation of the probability density function is performed bykernel-based approximation the Neural Network belongs to the category ofProbabilistic Neural Networks (PNN). The only control factor that needsto be selected for probabilistic neural network training is thesmoothing factor. An appropriate figure is chosen by experiment, byselecting a number that produces a low selection error, because PNNs arenot overly sensitive to the precise choice of smoothing factor. Thegreatest advantages of PNNs are the training speed and the fact that theoutput is probabilistic. Training a PNN may consist in the main part ofcopying training cases into the network, and so is as close toinstantaneous as can be expected. On the basis of obtained Data Set itis necessary to construct a specific PNN for each type of grade: HP, P,and F grade.

The selection criterion for PNN is very important from the practicalpoint of view. The PNN that is constructed for a specific Data Set isdistinguishing by its kernel-function only. The training of the PNNconsists of the generation of a great number of networks with adifferent value of this parameter. The selection criterion can be basedon the quality of classification but not on the value of the parameterof the kernel-function. The quality of classification (grading) for eachlevel of the grade is determined by the percentage of coincidencebetween the expert and PNN grades. The selection criterion can bepresented by linear convolution:${Q = {\sum\limits_{i = 1}^{5}{\alpha_{i} \cdot Q_{i}}}},{{\sum\limits_{i = 1}^{5}\alpha_{i}} = 1},$where:Q_(i)—percentage of coincidence between the expert and PNN grades forthe i-level of gradeα_(i)—weighting factor (priority) for the i-level of grade.If α_(i)=0.2 for i= 1,5 the selection criterion enables the PNN with thehighest total percentage of the coincidence grades to be chosen.

The problem of PNN training is to search for the network that providesthe highest value of grading quality—max Q for the given values of theweighting factor—α_(i), i= 1,5. Table 3 presents the percentage ofcoincidence grades for the PNN for HP-tested fabric (M&S P18B TestMethod). Table 3 contains the data for the best for the consecutionneural networks. The network-68 is the best network for the selectioncriteria Q₁₂₃₄ (α₁=α₂=α₃=α₄=0.25), Q₁₂₃ (α₁=α₂=α₃=0.33), and Q₁₂(α₁=α₂=0.50). If the criterion Q₂₃ (α₂=α₃=0.50) is used for theelection, the network-56 is the best network. TABLE 3 Percentage ofcoincidence between Expert and PNN grades Grades Number of PNN 1 2 3 4Q₁₂₃₄ Q₁₂ Q₂₃ Q₁₂₃ 2 58 76 59 86 279 134 135 193 22 45 86 69 54 254 131155 200 48 45 76 77 53 251 121 153 198 52 58 76 78 53 265 134 154 212 5645 86 75 67 273 131 161 206 61 61 71 82 46 260 132 153 214 68 65 81 7667 289 146 157 222

Attention is made now to FIGS. 9, 10 and 11 which illustrate thehistogram for HP grades for an original fabric (according to M&S P18BTest Method) obtained by experts and the network of the presentinvention (marked PNN), histograms for HP grades for the tested fabricfor M&S P18A and P18B Test Methods respectively. All histograms indicatevery similar distributions of the expert and PNN grades for the HPgrades.

Hairiness and Snagging Grading: As was shown above, H grading is basedon the calculation of the Hairiness Index−I_(H) and the estimation ofmembership to one of five subintervals of this index. For this approachit is necessary to determine the parameter of linear convolution—α andthe limits of subintervals I_(H), for i= 1,4. A special Data Set of 41different fabrics was prepared for this. The Data Set contains averagedgrades of three experts for original fabrics. Parameter of the linearconvolution and the limits of subintervals were determined by using asearch method. Minimum of the average value of absolute differencebetween the expert H grade and the calculated H grade was used as acriterion of this search method. The following values of the H-gradingalgorithm were obtained: α=0.1; I_(H1)=0.52; I_(H2)=0.47; I_(H3)=0.35;I_(H4)=0.30. Minimum of the absolute difference between the expertH-grade and the calculated H-grade is equal to 0.53. It should be notedthat this result was obtained on the basis of subjective expert grades.The experts used an imaginary scale of H-grades. This scale was notbased on pictures/holograms or any other specification of the H-gradingprocedure or rules.

A Data Set of 38 woven and single knitted fabrics was used forestimating the parameters of S grading algorithms: α, I_(S1), I_(S2),I_(S3), A_(L), and A_(S). As a result of the minimization of theabsolute difference between the Expert and SET-machine grades forsnagging and length of snags, the following values of parameters wereobtained: α=0.6, I_(S1)=10.6, I_(S2)=5.8, I_(S3)=3.8, A_(L)=2.5,A_(S)=1.

Test results: Comparative analysis of the expert and elaborated devicegrades is the main goal of the testing. The testing includes two stages:testing of correlation between expert and machine grades and testing ofrepeatability of the machine grades. The correlation test consists ofcomparison of the expert and machine grades for the following cases:

1. HP grade for one specimen of the original fabric

2. Average HP grade for four specimens of the tested fabric

3. Average P grade for four specimens of the tested fabric

4. Average F grade for four specimens of the tested fabric

The repeatability test consists of a comparison of HP, P, and F gradesfor a series of tests for the same specimen under the same conditions.The repeatability was estimated for the cycle average of fourconsecutive grades within the series.

Marks and Spencer M&S P18A Test Method: This test method is designatedfor single knitted fabric. The total number of the tested fabrics is 27.Tables 4-6 present the results of statistical analysis for HP-, P-, andF-grades. TABLE 4 Hologram/Picture grades Grouped Difference IntervalNumber of cases Percentage Percentage 0 <= difference <= 0.5 18 67 930.5 < difference <= 1 7 26 1 < difference 2 7 7

TABLE 5 Pilling grades Grouped Difference Interval Number of casesPercentage Percentage 0 <= difference <= 0.5 7 37 74 0.5 < difference <=1 7 37 1 < difference 5 26 26

TABLE 6 Fuzzing grades Grouped Difference Interval Number of casesPercentage Percentage 0 <= difference <= 0.5 13 62 100 0.5 < difference<= 1 8 38 1 < difference 0 0 0Results of the repeatability test are as follows: 100% for HP-grade, 91%for P-grade, and 100% for F-grade.

Marks and Spencer M&S P18B Test Method: This test method is designatedfor knitwear. The total number of tested fabrics is 60. Tables 7-9present the results of statistical analysis for HP, P, and F-grades.TABLE 7 Hologram/Picture grades Grouped Difference Interval Number ofcases Percentage Percentage 0 <= difference <= 0.5 15 75 100 0.5 <difference <= 1 5 25 1 < difference 0 0 0

TABLE 8 Pilling grades Grouped Difference Interval Number of casesPercentage Percentage 0 <= difference <= 0.5 17 85 100 0.5 < difference<= 1 3 15 1 < difference 0 0 0

TABLE 9 Fuzzing grades: Grouped Difference Interval Number of casesPercentage Percentage 0 <= difference <= 0.5 17 85 100 0.5 < difference<= 1 3 15 1 < difference 0 0 0The results of the repeatability test for M&S P18B Test Method are asfollows: 100% for HP grade, 100% for P grade, and 87% for F grade.

Hairiness Grading: This test method is designated for single knittedfabric. The total number of tested fabrics is 41. Table 10 presents theresults of statistical analysis for Hairiness: TABLE 10 Number ofDifference Interval cases Percentage Grouped Percentage 0 13 32 64 98 0< difference <= 0.5 13 32 0.5 < difference <= 1 14 34 34 1 < difference1 2 2 2The repeatability test shows 100% coincidence for the Hairiness grading.The grades of two independent experts are the same in 66% of the tests.This percentage can be interpreted as a repeatability index for theexpert grading of hairiness. Attention is made also to FIG. 12 whichpresents typical fabric images with the upper and lower limits of thehairiness that were obtained by Image Processing.

Snagging Grading: Table 11 presents results of testing for Snagging:TABLE 11 Grouped Difference I Number of cases Percentage Percentage 0 2771 97   0.5 10 26 1 1 3 3 Total 38 100 100Repeatability test for snagging was carried out on samples of fourfabrics with a different S grade. The number of tests for each fabricwas 5. The test results show 100% level for the Repeatability Index ofevery fabric.

We have described a new approach to objective evaluation for all typesof textiles surface defects: pilling, fuzziness, snagging, andhairiness. We present a new method of protrusion capturing, algorithmsof the image processing for the protrusions parameterization, and thegrading procedures are based on the neural networks for pilling andfuzziness or the thresholds for snagging and hairiness. The testingresults show the ability of the approach and the elaborated equipment toproduce high level reliable and consistent grades for all abovementioned tests of textiles surface quality. The obtained results allowelimination errors due to the subjective human grading procedure andprovide objective textile surface grades according to the existingstandards

The average value of the outliers' parameters for the entire set ofprofiles of the fabric sample represents the 5-D input of the network.The output of this Neural Network is the value of pilling degreecorresponding to a specific set of outlier parameters. The network isdesigned and trained on the data set. The data set was obtained byexpert estimations of the fabric's pilling according to the requirementsof ASTM D 3512-96 Standard Test Method for Pilling Resistance and OtherRelated Surface Changes of Textile Fabrics: Random Tumble PillingTester.

In various embodiments of the invention:

A method for the detection of fabric surface and surface protrusions andfor the classification of fabric quality according to the geometricalparameters and population density thereof, said method comprising thesteps

a. providing a sample of the textile to be classified;

b. bending said sample over a small diameter element;

c. generating a set-off two-dimensional image of the portion of thefabric sample being bent;

d. advancing said fabric sample by a small increment;

e. repeating steps c and d as often as necessary to scan said fabricsample;

f. counting the number of protrusions;

g. measuring and calculating the geometrical parameters of eachprotrusion;

h. calculating the degree or degrade of said protrusions by means of aneural network; and

The invention may further include the above mechanism wherein the fabricfolding is accomplished by a rotating or stationary tube.

The invention may further include the folding mechanism wherein thefabric is folded over a stationary or moving edge.

The invention may further include a detection device wherein anyelectro-optical device is used for capturing the image of the protrusionsilhouette and transfers it to the processor.

The invention may further include that in the background screen thecolor of its surface seen by the detection device, can be changed.

The invention may further include that the said background screencomprises the opacity or the translucency of the screen can be changedto enhance the contrast of the protrusion silhouette as seen by thedetection device.

The invention may further include the method and apparatus for thedetection of fabric surface protrusions as described whereinillumination is added to enhance the image captured by the detectiondevice.

The invention may further include the method and apparatus for thedetection of fabric surface protrusions as described wherein, the systemcan calculate the number of protrusions for a tested sample of fabric.

The invention may further include the method and apparatus for thedetection of fabric surface protrusions as described wherein the systemcan calculate the three-dimensional size of the protrusions for a testedsample of fabric.

The invention may further include the method and apparatus for thedetection of fabric surface protrusions as described wherein theprotrusions are the result of pilling of a fabric surface.

The invention may further include the method and apparatus for thedetection fabric surface protrusions as described wherein the parametersof protrusions are processed by a Neural-Network for pilling grading.

The invention may further include the method and apparatus for thedetection of fabric surface protrusions wherein the protrusions are theresult of hairiness of a fabric surface.

The invention may further include the method and apparatus for thedetection fabric of surface protrusions wherein the protrusions are theresult of foreign bodies on a fabric surface.

The invention may further include the method and apparatus for thedetection fabric of surface protrusions as described wherein theprotrusions are apparent on any pliable surface.

1. An apparatus for the grading of a tested fabric surface protrusionsand the classification of fabric quality according to the grades of alive expert comprising: a profile capturing unit to receive grey scaleimages of silhouettes of fold profiles of said tested fabric taken indefined increments along said tested fabric; an image processing unit toreceive said grey scale images and to identify protrusions for each suchsilhouette; and a pilling grading unit to receive said identifiedprotrusions and to match a grading value to at least one of listcomprising pilling value, hairiness value and fuzziness value.
 2. Theapparatus of claim 1 wherein said image processing unit is adapted toperform framing of the area of interest of said tested fabric, to definea threshold line separating between pills and fuzziness, and tostatistically calculate grading of the protrusions.
 3. The apparatus ofclaim 1 wherein said pilling grading unit comprises a neural networkcomputing platform, adapted to match grading of said tested fabric basedon data set representing samples of previously tested fabrics.
 4. Amethod for grading a tested fabric surface protrusions and for theclassification of fabric quality according to the grades of a liveexpert comprising: receiving a grey scale image of at least onesilhouette of said tested fabric; processing said grey scale image usingimage processing methods; and grading said tested fabric in accordancewith human grading scale using statistical calculations